Investigating transmission of HIV-1 drug resistance is essential to map current and predict potential future problems in the treatment of HIV-1 patients. If a patient develops resistance to an antiviral regimen and consequently needs to switch treatment, this represents not only a loss of therapeutic options for the patient but also an additional economical cost. Patients thus burn their regimens faster, have a reduced quality of life because of faster disease progression, more toxic side effects of drugs for salvage treatment, and a shorter life expectancy with a larger loss in productivity for the society. The detection of primary drug resistance in newly diagnosed patients, and the understanding and prevention of the occurrence of transmitted drug resistance is therefore not only a public health but also an economical problem.
Studies have shown that in absence of drug selective pressure, major drug resistance mutations tend to disappear in the circulating virus because of their fitness cost for the virus, but remain archived in the body. Transmitted drug resistance can therefore be present as a minority variant, not detectable by population sequencing. Such variants can then contribute to first line failure even when this therapy was optimized using resistance testing. Therefore we intend to develop the knowledge and framework that could be used to estimate markers of hidden transmitted drug resistance mutations, inform guidelines how to deal with this, and apply them in routine clinical practice. In that way, the 1st line therapy could be further optimized to avoid the selection of drug resistance mutations present in those minority populations.
Previous studies investigated the amount of transmitted drug resistance and tried to predict its future trends, but why and how transmission of drug resistance occurs, whether and how it is causing new ‘resistant’ epidemics, and how such variables contribute to treatment failure of the first line regimen needs more research. Not only epidemiological aspects of the infection should be considered, but also the biological and evolutionary dynamics of the virus, and the spatio-temporal and sociodemographic characteristics of transmitted drug resistance. This would allow predicting which mutations are more likely to be transmitted, under which genomic background such transmission should be expected, and where and how such transmissions take place.
In our research, we would like to understand, model and predict patterns of transmission of drug resistance, by combining deep sequencing, molecular epidemiology, mathematical modeling, longitudinal logistic regression and spatio-temporal modeling.
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